Overview

Dataset statistics

Number of variables9
Number of observations3192
Missing cells7224
Missing cells (%)25.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory408.5 KiB
Average record size in memory131.0 B

Variable types

NUM8
CAT1

Warnings

LandMaxTemperature is highly correlated with LandAverageTemperature and 2 other fieldsHigh correlation
LandAverageTemperature is highly correlated with LandMaxTemperature and 2 other fieldsHigh correlation
LandMinTemperature is highly correlated with LandAverageTemperature and 2 other fieldsHigh correlation
LandAndOceanAverageTemperature is highly correlated with LandAverageTemperature and 2 other fieldsHigh correlation
LandAndOceanAverageTemperatureUncertainty is highly correlated with LandAverageTemperatureUncertaintyHigh correlation
LandAverageTemperatureUncertainty is highly correlated with LandAndOceanAverageTemperatureUncertaintyHigh correlation
LandMaxTemperature has 1200 (37.6%) missing values Missing
LandMaxTemperatureUncertainty has 1200 (37.6%) missing values Missing
LandMinTemperature has 1200 (37.6%) missing values Missing
LandMinTemperatureUncertainty has 1200 (37.6%) missing values Missing
LandAndOceanAverageTemperature has 1200 (37.6%) missing values Missing
LandAndOceanAverageTemperatureUncertainty has 1200 (37.6%) missing values Missing
dt has unique values Unique

Reproduction

Analysis started2020-11-02 21:58:05.606378
Analysis finished2020-11-02 21:58:35.136258
Duration29.53 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

dt
Categorical

UNIQUE

Distinct3192
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
1891-05-01
 
1
1844-02-01
 
1
1931-01-01
 
1
1945-09-01
 
1
1831-08-01
 
1
Other values (3187)
3187 
ValueCountFrequency (%) 
1891-05-011< 0.1%
 
1844-02-011< 0.1%
 
1931-01-011< 0.1%
 
1945-09-011< 0.1%
 
1831-08-011< 0.1%
 
1981-04-011< 0.1%
 
1904-12-011< 0.1%
 
1833-07-011< 0.1%
 
1853-11-011< 0.1%
 
1964-07-011< 0.1%
 
1997-12-011< 0.1%
 
1762-11-011< 0.1%
 
1967-08-011< 0.1%
 
1767-01-011< 0.1%
 
1871-04-011< 0.1%
 
1860-12-011< 0.1%
 
1783-06-011< 0.1%
 
1836-04-011< 0.1%
 
1970-08-011< 0.1%
 
1975-10-011< 0.1%
 
1826-07-011< 0.1%
 
1840-02-011< 0.1%
 
1947-10-011< 0.1%
 
1833-04-011< 0.1%
 
1970-09-011< 0.1%
 
Other values (3167)316799.2%
 
2020-11-02T15:58:35.353756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3192 ?
Unique (%)100.0%
2020-11-02T15:58:35.624987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1815825.6%
 
0672821.1%
 
-638420.0%
 
821386.7%
 
921386.7%
 
715384.8%
 
212884.0%
 
59503.0%
 
69382.9%
 
38302.6%
 
48302.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2553680.0%
 
Dash Punctuation638420.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1815831.9%
 
0672826.3%
 
821388.4%
 
921388.4%
 
715386.0%
 
212885.0%
 
59503.7%
 
69383.7%
 
38303.3%
 
48303.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6384100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common31920100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1815825.6%
 
0672821.1%
 
-638420.0%
 
821386.7%
 
921386.7%
 
715384.8%
 
212884.0%
 
59503.0%
 
69382.9%
 
38302.6%
 
48302.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII31920100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1815825.6%
 
0672821.1%
 
-638420.0%
 
821386.7%
 
921386.7%
 
715384.8%
 
212884.0%
 
59503.0%
 
69382.9%
 
38302.6%
 
48302.6%
 

LandAverageTemperature
Real number (ℝ)

HIGH CORRELATION

Distinct2850
Distinct (%)89.6%
Missing12
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean8.374731132
Minimum-2.08
Maximum19.021
Zeros0
Zeros (%)0.0%
Memory size25.1 KiB
2020-11-02T15:58:35.989397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.08
5-th percentile1.97075
Q14.312
median8.6105
Q312.54825
95-th percentile14.395
Maximum19.021
Range21.101
Interquartile range (IQR)8.23625

Descriptive statistics

Standard deviation4.381309771
Coefficient of variation (CV)0.5231582604
Kurtosis-1.342072459
Mean8.374731132
Median Absolute Deviation (MAD)4.1565
Skewness-0.08142566548
Sum26631.645
Variance19.19587531
MonotocityNot monotonic
2020-11-02T15:58:36.351176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.29340.1%
 
13.76540.1%
 
2.73730.1%
 
3.09230.1%
 
14.19730.1%
 
13.82730.1%
 
13.45930.1%
 
12.24730.1%
 
13.74430.1%
 
13.48430.1%
 
13.82130.1%
 
14.31930.1%
 
2.03930.1%
 
3.21330.1%
 
14.74230.1%
 
13.95330.1%
 
13.41230.1%
 
3.78530.1%
 
3.09930.1%
 
13.5830.1%
 
5.27230.1%
 
14.24230.1%
 
8.72230.1%
 
3.9230.1%
 
11.09730.1%
 
Other values (2825)310397.2%
 
(Missing)120.4%
 
ValueCountFrequency (%) 
-2.081< 0.1%
 
-1.5031< 0.1%
 
-1.4311< 0.1%
 
-1.3851< 0.1%
 
-1.2491< 0.1%
 
-0.9781< 0.1%
 
-0.8371< 0.1%
 
-0.8111< 0.1%
 
-0.8061< 0.1%
 
-0.7931< 0.1%
 
ValueCountFrequency (%) 
19.0211< 0.1%
 
17.911< 0.1%
 
17.611< 0.1%
 
17.1151< 0.1%
 
16.8211< 0.1%
 
16.5211< 0.1%
 
16.4681< 0.1%
 
16.3911< 0.1%
 
16.1831< 0.1%
 
16.0251< 0.1%
 

LandAverageTemperatureUncertainty
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1594
Distinct (%)50.1%
Missing12
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.9384679245
Minimum0.034
Maximum7.88
Zeros0
Zeros (%)0.0%
Memory size25.1 KiB
2020-11-02T15:58:36.690263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.034
5-th percentile0.066
Q10.18675
median0.392
Q31.41925
95-th percentile3.2351
Maximum7.88
Range7.846
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation1.096439795
Coefficient of variation (CV)1.168329536
Kurtosis3.536050467
Mean0.9384679245
Median Absolute Deviation (MAD)0.31
Skewness1.780596521
Sum2984.328
Variance1.202180224
MonotocityNot monotonic
2020-11-02T15:58:37.002139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.087200.6%
 
0.064190.6%
 
0.077160.5%
 
0.078160.5%
 
0.086140.4%
 
0.068140.4%
 
0.082140.4%
 
0.085130.4%
 
0.084130.4%
 
0.07120.4%
 
0.083120.4%
 
0.106120.4%
 
0.09110.3%
 
0.104110.3%
 
0.276110.3%
 
0.062110.3%
 
0.091110.3%
 
0.079110.3%
 
0.099110.3%
 
0.08110.3%
 
0.059110.3%
 
0.089110.3%
 
0.072100.3%
 
0.066100.3%
 
0.096100.3%
 
Other values (1569)286589.8%
 
(Missing)120.4%
 
ValueCountFrequency (%) 
0.0341< 0.1%
 
0.03520.1%
 
0.0361< 0.1%
 
0.0391< 0.1%
 
0.041< 0.1%
 
0.04130.1%
 
0.04220.1%
 
0.04420.1%
 
0.04530.1%
 
0.04630.1%
 
ValueCountFrequency (%) 
7.881< 0.1%
 
7.4921< 0.1%
 
7.3491< 0.1%
 
6.4151< 0.1%
 
6.3411< 0.1%
 
5.9661< 0.1%
 
5.9631< 0.1%
 
5.8571< 0.1%
 
5.8271< 0.1%
 
5.6561< 0.1%
 

LandMaxTemperature
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1821
Distinct (%)91.4%
Missing1200
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean14.3506009
Minimum5.9
Maximum21.32
Zeros0
Zeros (%)0.0%
Memory size25.1 KiB
2020-11-02T15:58:37.325098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile8.118
Q110.212
median14.76
Q318.4515
95-th percentile20.1625
Maximum21.32
Range15.42
Interquartile range (IQR)8.2395

Descriptive statistics

Standard deviation4.309578966
Coefficient of variation (CV)0.3003065164
Kurtosis-1.456171165
Mean14.3506009
Median Absolute Deviation (MAD)4.137
Skewness-0.09693800875
Sum28586.397
Variance18.57247086
MonotocityNot monotonic
2020-11-02T15:58:37.661909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17.19630.1%
 
19.8530.1%
 
8.55530.1%
 
19.3630.1%
 
17.28930.1%
 
20.03730.1%
 
19.62830.1%
 
10.78130.1%
 
19.75330.1%
 
17.71330.1%
 
11.41130.1%
 
11.2420.1%
 
11.38420.1%
 
19.16320.1%
 
18.07820.1%
 
19.02220.1%
 
10.46920.1%
 
11.62820.1%
 
19.8220.1%
 
19.98720.1%
 
17.39420.1%
 
8.54420.1%
 
19.27520.1%
 
19.39620.1%
 
17.76920.1%
 
Other values (1796)193160.5%
 
(Missing)120037.6%
 
ValueCountFrequency (%) 
5.91< 0.1%
 
6.4211< 0.1%
 
6.4361< 0.1%
 
6.6421< 0.1%
 
6.6791< 0.1%
 
6.6861< 0.1%
 
6.8641< 0.1%
 
6.9611< 0.1%
 
7.0231< 0.1%
 
7.0641< 0.1%
 
ValueCountFrequency (%) 
21.321< 0.1%
 
21.1991< 0.1%
 
21.1081< 0.1%
 
21.0851< 0.1%
 
21.00620.1%
 
20.9721< 0.1%
 
20.971< 0.1%
 
20.9231< 0.1%
 
20.9221< 0.1%
 
20.9051< 0.1%
 

LandMaxTemperatureUncertainty
Real number (ℝ≥0)

MISSING

Distinct841
Distinct (%)42.2%
Missing1200
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean0.4797816265
Minimum0.044
Maximum4.373
Zeros0
Zeros (%)0.0%
Memory size25.1 KiB
2020-11-02T15:58:38.055281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.044
5-th percentile0.083
Q10.142
median0.252
Q30.539
95-th percentile1.86245
Maximum4.373
Range4.329
Interquartile range (IQR)0.397

Descriptive statistics

Standard deviation0.5832029575
Coefficient of variation (CV)1.215559174
Kurtosis7.55348287
Mean0.4797816265
Median Absolute Deviation (MAD)0.136
Skewness2.565863891
Sum955.725
Variance0.3401256896
MonotocityNot monotonic
2020-11-02T15:58:38.398056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.093140.4%
 
0.105120.4%
 
0.098110.3%
 
0.13110.3%
 
0.106110.3%
 
0.094110.3%
 
0.179100.3%
 
0.099100.3%
 
0.16100.3%
 
0.0990.3%
 
0.10890.3%
 
0.11790.3%
 
0.08890.3%
 
0.12290.3%
 
0.11690.3%
 
0.08590.3%
 
0.16590.3%
 
0.21990.3%
 
0.07990.3%
 
0.21290.3%
 
0.2880.3%
 
0.11180.3%
 
0.29880.3%
 
0.09280.3%
 
0.08480.3%
 
Other values (816)175354.9%
 
(Missing)120037.6%
 
ValueCountFrequency (%) 
0.0441< 0.1%
 
0.0481< 0.1%
 
0.0521< 0.1%
 
0.05530.1%
 
0.0561< 0.1%
 
0.05720.1%
 
0.05820.1%
 
0.05920.1%
 
0.0620.1%
 
0.0611< 0.1%
 
ValueCountFrequency (%) 
4.3731< 0.1%
 
4.241< 0.1%
 
4.1641< 0.1%
 
3.7511< 0.1%
 
3.4911< 0.1%
 
3.361< 0.1%
 
3.3391< 0.1%
 
3.1881< 0.1%
 
3.1871< 0.1%
 
3.1841< 0.1%
 

LandMinTemperature
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1872
Distinct (%)94.0%
Missing1200
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean2.743595382
Minimum-5.407
Maximum9.715
Zeros0
Zeros (%)0.0%
Memory size25.1 KiB
2020-11-02T15:58:38.743351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-5.407
5-th percentile-3.32445
Q1-1.3345
median2.9495
Q36.77875
95-th percentile8.51045
Maximum9.715
Range15.122
Interquartile range (IQR)8.11325

Descriptive statistics

Standard deviation4.15583532
Coefficient of variation (CV)1.514740602
Kurtosis-1.433529954
Mean2.743595382
Median Absolute Deviation (MAD)4.088
Skewness-0.05025501431
Sum5465.242
Variance17.27096721
MonotocityNot monotonic
2020-11-02T15:58:39.116385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7.89230.1%
 
8.16130.1%
 
7.81830.1%
 
8.18430.1%
 
-1.13930.1%
 
7.05720.1%
 
-3.2320.1%
 
5.17620.1%
 
-1.94220.1%
 
-3.76420.1%
 
1.9120.1%
 
7.96520.1%
 
3.16220.1%
 
0.55520.1%
 
5.26320.1%
 
-2.45220.1%
 
8.20520.1%
 
3.820.1%
 
-0.65420.1%
 
7.79620.1%
 
5.20220.1%
 
6.59720.1%
 
5.96720.1%
 
8.01220.1%
 
8.18920.1%
 
Other values (1847)193760.7%
 
(Missing)120037.6%
 
ValueCountFrequency (%) 
-5.4071< 0.1%
 
-5.3451< 0.1%
 
-4.9471< 0.1%
 
-4.7171< 0.1%
 
-4.6781< 0.1%
 
-4.6211< 0.1%
 
-4.5581< 0.1%
 
-4.5191< 0.1%
 
-4.4651< 0.1%
 
-4.3651< 0.1%
 
ValueCountFrequency (%) 
9.7151< 0.1%
 
9.6841< 0.1%
 
9.5691< 0.1%
 
9.5511< 0.1%
 
9.4821< 0.1%
 
9.4561< 0.1%
 
9.4281< 0.1%
 
9.4091< 0.1%
 
9.4071< 0.1%
 
9.3441< 0.1%
 

LandMinTemperatureUncertainty
Real number (ℝ≥0)

MISSING

Distinct781
Distinct (%)39.2%
Missing1200
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean0.4318488956
Minimum0.045
Maximum3.498
Zeros0
Zeros (%)0.0%
Memory size25.1 KiB
2020-11-02T15:58:39.503172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.045
5-th percentile0.08455
Q10.155
median0.279
Q30.45825
95-th percentile1.3948
Maximum3.498
Range3.453
Interquartile range (IQR)0.30325

Descriptive statistics

Standard deviation0.4458378371
Coefficient of variation (CV)1.032393139
Kurtosis7.0548683
Mean0.4318488956
Median Absolute Deviation (MAD)0.135
Skewness2.384389692
Sum860.243
Variance0.198771377
MonotocityNot monotonic
2020-11-02T15:58:39.873734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.237120.4%
 
0.13110.3%
 
0.082110.3%
 
0.145110.3%
 
0.126110.3%
 
0.213100.3%
 
0.224100.3%
 
0.338100.3%
 
0.12790.3%
 
0.12590.3%
 
0.08680.3%
 
0.31380.3%
 
0.13480.3%
 
0.09280.3%
 
0.08580.3%
 
0.11380.3%
 
0.23580.3%
 
0.34580.3%
 
0.1780.3%
 
0.11580.3%
 
0.21680.3%
 
0.25280.3%
 
0.15580.3%
 
0.24780.3%
 
0.19370.2%
 
Other values (756)176955.4%
 
(Missing)120037.6%
 
ValueCountFrequency (%) 
0.0451< 0.1%
 
0.0471< 0.1%
 
0.05130.1%
 
0.0531< 0.1%
 
0.05420.1%
 
0.0551< 0.1%
 
0.0581< 0.1%
 
0.0630.1%
 
0.06120.1%
 
0.06220.1%
 
ValueCountFrequency (%) 
3.4981< 0.1%
 
3.4281< 0.1%
 
2.9631< 0.1%
 
2.9291< 0.1%
 
2.8431< 0.1%
 
2.8221< 0.1%
 
2.7951< 0.1%
 
2.7141< 0.1%
 
2.5941< 0.1%
 
2.561< 0.1%
 

LandAndOceanAverageTemperature
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1616
Distinct (%)81.1%
Missing1200
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean15.21256576
Minimum12.475
Maximum17.611
Zeros0
Zeros (%)0.0%
Memory size25.1 KiB
2020-11-02T15:58:40.477222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12.475
5-th percentile13.3
Q114.047
median15.251
Q316.39625
95-th percentile17.0166
Maximum17.611
Range5.136
Interquartile range (IQR)2.34925

Descriptive statistics

Standard deviation1.274092954
Coefficient of variation (CV)0.08375266699
Kurtosis-1.322464368
Mean15.21256576
Median Absolute Deviation (MAD)1.179
Skewness-0.05604937795
Sum30303.431
Variance1.623312857
MonotocityNot monotonic
2020-11-02T15:58:40.857378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15.00550.2%
 
13.31140.1%
 
16.78340.1%
 
13.2640.1%
 
16.49640.1%
 
16.84640.1%
 
16.59640.1%
 
15.92740.1%
 
16.91230.1%
 
16.63430.1%
 
14.60730.1%
 
13.72430.1%
 
14.16130.1%
 
16.21630.1%
 
14.04730.1%
 
16.60530.1%
 
14.35930.1%
 
15.82330.1%
 
15.12630.1%
 
13.90430.1%
 
16.8330.1%
 
14.41830.1%
 
16.43730.1%
 
16.74530.1%
 
13.5430.1%
 
Other values (1591)190859.8%
 
(Missing)120037.6%
 
ValueCountFrequency (%) 
12.4751< 0.1%
 
12.621< 0.1%
 
12.6581< 0.1%
 
12.7021< 0.1%
 
12.7321< 0.1%
 
12.8281< 0.1%
 
12.8331< 0.1%
 
12.8391< 0.1%
 
12.841< 0.1%
 
12.8791< 0.1%
 
ValueCountFrequency (%) 
17.6111< 0.1%
 
17.6091< 0.1%
 
17.6071< 0.1%
 
17.5891< 0.1%
 
17.5781< 0.1%
 
17.5681< 0.1%
 
17.5321< 0.1%
 
17.5081< 0.1%
 
17.50320.1%
 
17.4911< 0.1%
 

LandAndOceanAverageTemperatureUncertainty
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct294
Distinct (%)14.8%
Missing1200
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean0.1285321285
Minimum0.042
Maximum0.457
Zeros0
Zeros (%)0.0%
Memory size25.1 KiB
2020-11-02T15:58:41.222466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.042
5-th percentile0.052
Q10.063
median0.122
Q30.151
95-th percentile0.28345
Maximum0.457
Range0.415
Interquartile range (IQR)0.088

Descriptive statistics

Standard deviation0.07358679601
Coefficient of variation (CV)0.5725167462
Kurtosis1.525069706
Mean0.1285321285
Median Absolute Deviation (MAD)0.0535
Skewness1.275594309
Sum256.036
Variance0.005415016546
MonotocityNot monotonic
2020-11-02T15:58:41.525038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.061491.5%
 
0.059471.5%
 
0.06451.4%
 
0.062441.4%
 
0.057411.3%
 
0.058391.2%
 
0.056371.2%
 
0.063351.1%
 
0.054351.1%
 
0.064311.0%
 
0.122300.9%
 
0.129280.9%
 
0.127280.9%
 
0.125270.8%
 
0.052270.8%
 
0.053260.8%
 
0.05250.8%
 
0.126250.8%
 
0.128250.8%
 
0.137230.7%
 
0.134230.7%
 
0.051220.7%
 
0.13220.7%
 
0.055220.7%
 
0.065220.7%
 
Other values (269)121438.0%
 
(Missing)120037.6%
 
ValueCountFrequency (%) 
0.0421< 0.1%
 
0.0431< 0.1%
 
0.04530.1%
 
0.04630.1%
 
0.04740.1%
 
0.048120.4%
 
0.049130.4%
 
0.05250.8%
 
0.051220.7%
 
0.052270.8%
 
ValueCountFrequency (%) 
0.4571< 0.1%
 
0.4421< 0.1%
 
0.4381< 0.1%
 
0.4271< 0.1%
 
0.4171< 0.1%
 
0.4141< 0.1%
 
0.4021< 0.1%
 
0.3891< 0.1%
 
0.3871< 0.1%
 
0.37820.1%
 

Interactions

2020-11-02T15:58:12.938762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:13.281563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:13.599995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:13.913555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:14.220909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:14.552580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:14.862349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:15.163822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:15.472518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:15.807207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:16.127301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:16.445813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:16.746346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:17.065603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:17.386717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:17.689506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:17.989578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:18.312052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:18.666778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:19.006572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:19.329687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:19.642962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:19.950042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:20.284200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:20.622474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:21.013648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:21.328254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:21.587024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:21.852110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:22.136489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:22.440037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:22.711710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:22.987088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:23.330243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:23.670222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:23.998320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:24.309597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:24.617801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:24.914745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:25.215643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:25.513942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:25.832646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:26.159510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:26.499536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:26.805622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:27.130549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:27.472763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:27.797551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:28.113427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:28.430547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:28.733358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:29.015101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:29.295699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:29.568096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:29.792987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:30.071573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:30.353106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:30.642164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:30.934253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:31.377533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:31.652871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:31.938135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:32.227195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:32.527337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-02T15:58:41.771593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-02T15:58:42.270170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-02T15:58:42.747608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-02T15:58:43.252357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-02T15:58:33.113338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:33.686996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:34.273295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-02T15:58:34.812608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
01750-01-013.0343.574NaNNaNNaNNaNNaNNaN
11750-02-013.0833.702NaNNaNNaNNaNNaNNaN
21750-03-015.6263.076NaNNaNNaNNaNNaNNaN
31750-04-018.4902.451NaNNaNNaNNaNNaNNaN
41750-05-0111.5732.072NaNNaNNaNNaNNaNNaN
51750-06-0112.9371.724NaNNaNNaNNaNNaNNaN
61750-07-0115.8681.911NaNNaNNaNNaNNaNNaN
71750-08-0114.7502.231NaNNaNNaNNaNNaNNaN
81750-09-0111.4132.637NaNNaNNaNNaNNaNNaN
91750-10-016.3672.668NaNNaNNaNNaNNaNNaN

Last rows

dtLandAverageTemperatureLandAverageTemperatureUncertaintyLandMaxTemperatureLandMaxTemperatureUncertaintyLandMinTemperatureLandMinTemperatureUncertaintyLandAndOceanAverageTemperatureLandAndOceanAverageTemperatureUncertainty
31822015-03-016.7400.06012.6590.0960.8940.07915.1930.061
31832015-04-019.3130.08815.2240.1373.4020.14715.9620.061
31842015-05-0112.3120.08118.1810.1176.3130.15316.7740.058
31852015-06-0114.5050.06820.3640.1338.6270.16817.3900.057
31862015-07-0115.0510.08620.9040.1099.3260.22517.6110.058
31872015-08-0114.7550.07220.6990.1109.0050.17017.5890.057
31882015-09-0112.9990.07918.8450.0887.1990.22917.0490.058
31892015-10-0110.8010.10216.4500.0595.2320.11516.2900.062
31902015-11-017.4330.11912.8920.0932.1570.10615.2520.063
31912015-12-015.5180.10010.7250.1540.2870.09914.7740.062